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Orientador(es)
Resumo(s)
The widespread adoption of Internet of Things (IoT) technologies within Industry 4.0 has increased the need for more responsive and efficient intralogistics operations, especially as industries move toward highly customized, small-batch production. Autonomous Mobile Robots (AMRs) provide the flexibility needed for dynamic industrial environments. However, their effective integration relies on accurate task-duration estimates, which are difficult to obtain due to system variability and operational uncertainty. This work addresses this challenge by statistically modeling picking and loading/unloading times using real-world industrial data. Probability distribution-fitting techniques are employed to capture the random nature of these operations, thereby reducing the need for unrealistic assumptions often used in scheduling models. The proposed methodology enhances the statistical basis for robust AMR fleet scheduling, improving coordination and reducing operational variability. Validation is conducted by directly comparing empirical data with fitted distributions, demonstrating clear improvements in representativeness and predictive accuracy. The results demonstrate the potential of this approach to improve decision-making in highly dynamic intralogistics environments.
Descrição
Palavras-chave
Intralogistics analytics Operational efficiency Robust scheduling Statistical distribution fitting Performance analysis Discrete events
Contexto Educativo
Citação
Editora
Licença CC
Sem licença CC